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At least 1,099 records · Page 61Linked to original sources
USGS2021

Ensemble species distribution model identifies survey opportunities for at-risk bearded beaksedge (Rhynchospora crinipes) in the southeastern United States

Locating additional occurrences of at-risk species can inform assessments of their status and conservation needs (including potential legal protections). The perennial bearded beaksedge ( Rhynchospora crinipes ) ranges from Mississippi to North Carolina, but known occurrences are limited. Because of the species' apparent rarity, a model to identify areas with suitable habitat conditions for the species will allow conservationists to effectively prioritize and allocate scarce surveying resources. We used known occurrence records, a suite of environmental datasets, and four species distribution modeling techniques (generalized additive, GAM; maximum entropy, MaxEnt; generalized boosted, GBM; and weighted ensemble) to generate maps to inform surveys for R. crinipes . The ensemble approach improved predictive performance (AUC-PR = 0.95) compared to other techniques (individual model AUC-PR ranged from 0.7 to 0.8). We also obtained quantitative insights on the species' habitat relationships, including the likelihood of R. crinipes 's presence near Atlantic white cedar ( Chamaecyparis thyoides ) habitat and floodplains, which is consistent with prior field observations. The ensemble model indicated that 3.6% of the study area could be suitable habitat, but only 0.38% had high suitability. Small stream riparian habitats and Atlantic swamp forests in Alabama, Florida, and Georgia had the highest proportion of suitable areas. Prioritizing surveys in areas with model-indicated high habitat suitability is expected to reveal additional R. crinipes occurrences. We suggest surveying efforts for other at-risk species may benefit from using an ensemble modeling approach to identify and prioritize survey areas and improve ecological knowledge of these species.

Alabama, Florida, Georgia, Mississippi, North Caro↗
USGS2007

Environmental and ecological conditions surrounding the production of large year classes of walleye ( Sander vitreus ) in Saginaw Bay, Lake Huron

The Saginaw Bay walleye population ( Sander vitreus ) has not fully recovered from a collapse that began in the 1940s and has been dependent on stocking with only limited natural reproduction. Beginning in 2003, and through at least 2005, reproductive success of walleye surged to unprecedented levels. The increase was concurrent with ecological changes in Lake Huron and we sought to quantitatively model which factors most influenced this new dynamic. We developed Ricker stock-recruitment models for both wild and stock fish and evaluated them with second-order Akaike's information criterion to find the best model. Independent variables included adult alewife ( Alosa pseudoharengus ) abundance, spring water temperatures, chlorophyll a levels and total phosphorus levels. In all, 14 models were evaluated for production of wild age-0 walleyes and eight models for stocked age-0 walleyes. For wild walleyes, adult alewife abundance was the dominant factor, accounting for 58% of the variability in age-0 abundance. Production of wild age-0 fish increased when adult alewives were scarce. The only other plausible factor was spring water temperature. Predictably, alewife abundance was not important to stocked fish; instead temperature and adult walleye abundance were more significant variables. The surge in reproductive success for walleyes during 2003–2005 was most likely due to large declines in adult alewives in Lake Huron. While relatively strong year classes (age-1 and up) have been produced as a result of increased age-0 production during 2003–2005, the overall magnitude has not been as great as the initial age-0 abundance originally suggested. It appears that over-winter mortality is higher than in the past and may stem from higher predation or slower growth (lower condition for enduring winter thermal stress). From this it appears that low alewife abundance does not assure strong walleye year classes in Saginaw Bay but may be a prerequisite for them.

Journal of Great Lakes Research↗
USGS2009

One size does not fit all: Adapting mark-recapture and occupancy models for state uncertainty

Multistate capture?recapture models continue to be employed with greater frequency to test hypotheses about metapopulation dynamics and life history, and more recently disease dynamics. In recent years efforts have begun to adjust these models for cases where there is uncertainty about an animal?s state upon capture. These efforts can be categorized into models that permit misclassification between two states to occur in either direction or one direction, where state is certain for a subset of individuals or is always uncertain, and where estimation is based on one sampling occasion per period of interest or multiple sampling occasions per period. State uncertainty also arises in modeling patch occupancy dynamics. I consider several case studies involving bird and marine mammal studies that illustrate how misclassified states can arise, and outline model structures for properly utilizing the data that are produced. In each case misclassification occurs in only one direction (thus there is a subset of individuals or patches where state is known with certainty), and there are multiple sampling occasions per period of interest. For the cases involving capture?recapture data I allude to a general model structure that could include each example as a special case. However, this collection of cases also illustrates how difficult it is to develop a model structure that can be directly useful for answering every ecological question of interest and account for every type of data from the field.

Book chapter↗
USGS2018

Estimation of stream conditions in tributaries of the Klamath River, northern California

Because of their critical ecological role, stream temperature and discharge are requisite inputs for models of salmonid population dynamics. Coho Salmon inhabiting the Klamath Basin spend much of their freshwater life cycle inhabiting tributaries, but environmental data are often absent or only seasonally available at these locations. To address this information gap, we constructed daily averaged water temperature models that used simulated meteorological data to estimate daily tributary temperatures, and we used flow differentials recorded on the mainstem Klamath River to estimate daily tributary discharge. Observed temperature data were available for fourteen of the major salmon bearing tributaries, which enabled estimation of tributary-specific model parameters at those locations. Water temperature data from six mid-Klamath Basin tributaries were used to estimate a global set of parameters for predicting water temperatures in the remaining tributaries. The resulting parameter sets were used to simulate water temperatures for each of 75 tributaries from 1980-2015. Goodness-of-fit statistics computed from a cross-validation analysis demonstrated a high precision of the tributary-specific models in predicting temperature in unobserved years and of the global model in predicting temperatures in unobserved streams. Klamath River discharge has been monitored by four gages that broadly intersperse the 292 kilometers from the Iron Gate Dam to the Klamath River mouth. These gages defined the upstream and downstream margins of three reaches. Daily discharge of tributaries within a reach was estimated from 1980-2015 based on drainage-area proportionate allocations of the discharge differential between the upstream and downstream margin. Comparisons with measured discharge on Indian Creek, a moderate-sized tributary with naturally regulated flows, revealed that the estimates effectively approximated both the variability and magnitude of discharge.

California↗
USGS2020

Machine-learning models to map pH and redox conditions in groundwater in a layered aquifer system, Northern Atlantic Coastal Plain, eastern USA

Study region The study was conducted in the Northern Atlantic Coastal Plain aquifer system, in the eastern USA. Study focus Groundwater pH and redox conditions are fundamental chemical characteristics controlling the distribution of many contaminants of concern for drinking water or the ecological health of receiving waters. In this study, pH and redox conditions were modeled and mapped in a complex, layered aquifer system. Machine-learning methods (boosted regression trees) were applied to data from 3000 to 5000 wells. Predicted pH and the probability of anoxic conditions, defined by three thresholds of dissolved oxygen (0.5, 1, and 2 mg/L), were mapped at the 1-km 2 scale for each of 10 regional aquifer layers. New Hydrological Insights for the Region Maps depict the extent of acidic groundwater and oxic conditions in the shallow, unconfined surficial aquifer and in unconfined, recharge-proximal areas of underlying aquifers, in contrast to alkaline and anoxic groundwater elsewhere. Geographic patterns and influential predictors–including elevation, overlying confining-units thickness, and simulated groundwater age and flux–are consistent with prior understanding of the processes controlling pH and redox in the aquifer system. The model-based maps support robust estimates of aquifer proportions, either areal or volumetric, likely to contain groundwater of a specified quality or be vulnerable to specific pH- or redox-sensitive contaminants. The machine-learning methods were an effective tool to map groundwater quality at the regional scale.

New Jersey, Maryland, Delaware, Virginia↗
USGS2013

Great Lakes rivermouth ecosystems: scientific synthesis and management implications

At the interface of the Great Lakes and their tributary rivers lies the rivermouths, a class of aquatic ecosystem where lake and lotic processes mix and distinct features emerge. Many rivermouths are the focal point of both human interaction with the Great Lakes and human impacts to the lakes; many cities, ports, and beaches are located in rivermouth ecosystems, and these human pressures often degrade key ecological functions that rivermouths provide. Despite their ecological uniqueness and apparent economic importance, there has been relatively little research on these ecosystems as a class relative to studies on upstream rivers or the open-lake waters. Here we present a synthesis of current knowledge about ecosystem structure and function in Great Lakes rivermouths based on studies in both Laurentian rivermouths, coastal wetlands, and marine estuarine systems. A conceptual model is presented that establishes a common semantic framework for discussing the characteristic spatial features of rivermouths. This model then is used to conceptually link ecosystem structure and function to ecological services provided by rivermouths. This synthesis helps identify the critical gaps in understanding rivermouth ecology. Specifically, additional information is needed on how rivermouths collectively influence the Great Lakes ecosystem, how human alterations influence rivermouth functions, and how ecosystem services provided by rivermouths can be managed to benefit the surrounding socioeconomic networks.

Great Lakes↗
USGS2016

Identification of landscape features influencing gene flow: How useful are habitat selection models?

Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modeling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is important to consider explicit assumptions and spatial scales of measurement. We calculated pairwise genetic distance among 301 Dall's sheep (Ovis dalli dalli) in southcentral Alaska using an intensive noninvasive sampling effort and 15 microsatellite loci. We used multiple regression of distance matrices to assess the correlation of pairwise genetic distance and landscape resistance derived from an RSF, and combinations of landscape features hypothesized to influence dispersal. Dall's sheep gene flow was positively correlated with steep slopes, moderate peak normalized difference vegetation indices (NDVI), and open land cover. Whereas RSF covariates were significant in predicting genetic distance, the RSF model itself was not significantly correlated with Dall's sheep gene flow, suggesting that certain habitat features important during summer (rugged terrain, mid-range elevation) were not influential to effective dispersal. This work underscores that consideration of both habitat selection and landscape genetics models may be useful in developing management strategies to both meet the immediate survival of a species and allow for long-term genetic connectivity.

Evolutionary Applications↗
USGS2019

oSCR: A spatial capture–recapture R package for inference about spatial ecological processes

Spatial capture–recapture (SCR) methods have become widely applied in ecology. The immediate adoption of SCR is due to the fact that it resolves some major criticisms of traditional capture–recapture methods related to heterogeneity in detectabililty, and the emergence of new technologies (e.g. camera traps, non‐invasive genetics) that have vastly improved our ability to collection spatially explicit observation data on individuals. However, the utility of SCR methods reaches far beyond simply convenience and data availability. SCR presents a formal statistical framework that can be used to test explicit hypotheses about core elements of population and landscape ecology, and has profound implications for how we study animal populations. In this software note, we describe the technical basis and analytical workflow of oSCR, an R package for analyzing spatial encounter history data using a multi‐session sex‐structured likelihood. The impetus for developing oSCR was to create an accessible and transparent analysis tool that allows users to conveniently and intuitively formulate statistical models that map directly to fundamental processes of interest in spatial population ecology (e.g. space use, resource selection, density and connectivity). We have placed an emphasis on creating a transparent and accessible code base that is coupled with a logical workflow that we hope stimulates active participation in further technical developments.

Ecography↗
USGS2015

Combining state-and-transition simulations and species distribution models to anticipate the effects of climate change

State-and-transition simulation models (STSMs) are known for their ability to explore the combined effects of multiple disturbances, ecological dynamics, and management actions on vegetation. However, integrating the additional impacts of climate change into STSMs remains a challenge. We address this challenge by combining an STSM with species distribution modeling (SDM). SDMs estimate the probability of occurrence of a given species based on observed presence and absence locations as well as environmental and climatic covariates. Thus, in order to account for changes in habitat suitability due to climate change, we used SDM to generate continuous surfaces of species occurrence probabilities. These data were imported into ST-Sim, an STSM platform, where they dictated the probability of each cell transitioning between alternate potential vegetation types at each time step. The STSM was parameterized to capture additional processes of vegetation growth and disturbance that are relevant to a keystone species in the Greater Yellowstone Ecosystem—whitebark pine ( Pinus albicaulis ). We compared historical model runs against historical observations of whitebark pine and a key disturbance agent (mountain pine beetle, Dendroctonus ponderosae ), and then projected the simulation into the future. Using this combination of correlative and stochastic simulation models, we were able to reproduce historical observations and identify key data gaps. Results indicated that SDMs and STSMs are complementary tools, and combining them is an effective way to account for the anticipated impacts of climate change, biotic interactions, and disturbances, while also allowing for the exploration of management options.

AIMS Environmental Science↗
USGS2009

A landscape indicator approach to the identification and articulation of the consequences of land-cover change in the Mid-Atlantic Region, 1973-2001

Landscape indicators, derived from land-use and land-cover data, hydrology, nitrate deposition, and elevation data, were used by Jones and others (2001a) to calculate the ecological consequences of land-cover change. Nitrate loading and physical bird habitat were modeled from 1973 and 1992 land-cover and other spatial data for the Mid-Atlantic region. Utilizing the same methods, this study extends the analysis another decade with the use of the 2001 National Land Cover Dataset. Land-cover statistics and trends are calculated for three time periods: 1973-1992, 1992-2001 and 1973-2001. In addition, high-resolution aerial photographs (1 meter or better ground-sample distance) were acquired and analyzed for thirteen pairs of adjacent USGS 7.5 minute quadrangle maps in areas where distinct positive or negative changes to nitrogen loading and bird habitat were previously calculated. During the entire 30 year period, the data show that there was extensive loss of agriculture and forest area and a major increase in urban land-cover classes. However, the majority of the conversion of other classes to urban occurred during the 1992-2001 period. During the 1973-1992 period, there was only moderate increase in urban area, while there was an inverse relationship between agricultural change and forest change. In general, forest gain and agricultural loss was found in areas of improving landscape indicators, and forest loss and agricultural gain was found to occur in areas of declining indicators related to habitat and nitrogen loadings, which was generally confirmed by the aerial photographic analysis. In terms of the specific model results, bird habitat, which is mainly related to the extent of forest cover, declined overall with forest extent, but was also affected more in the decline of habitat quality. Nitrate loading, which is mainly related to agricultural land cover actually improved from 1992-2001, and in the overall study, mainly due to the conversion of agriculture to forests and urban. The high-resolution imagery analysis was significant in that it confirmed, at a very local level, the specific land-cover changes that were driving the landscape metrics and model results that were calculated from moderate resolution land-cover data and models. These were generally subtle changes in patch size of agriculture, forest, and urban areas, but had substantial effects on bird habitat and nitrogen loadings. This analysis of high-resolution imagery demonstrates and confirms the important ability of moderate-resolution land-cover data to capture significant landscape-level activity that is directly related to specific metrics of ecological significance. It also demonstrates consistent landscape-scale relationships between data derived from high-resolution, moderate-resolution and landscape-model sources. Finally, many of the areas of improvement and decline in bird habitat and nitrogen loadings appear to be potentially regional in nature and likely reflect some local trend in landscape activity. Although the use of ecoregions as sampling units has been criticized in recent years, these results show that basic changes in Level 1 land-cover categories, such as forest and agriculture, may still reflect ecoregional patterns and considerations at some scale of mapping and analysis. This is a potentially important area for future landscape-indicator research. This and other follow-on research opportunities are discussed.

Open-File Report↗
USGS2010

Interfacing models of wildlife habitat and human development to predict the future distribution of puma habitat

The impact of human land uses on ecological systems typically differ relative to how extensively natural conditions are modified. Exurban development is intermediate-intensity residential development that often occurs in natural landscapes. Most species-habitat models do not evaluate the effects of such intermediate levels of human development and even fewer predict how future development patterns might affect the amount and configuration of habitat. We addressed these deficiencies by interfacing a habitat model with a spatially-explicit housing-density model to study the effect of human land uses on the habitat of pumas ( Puma concolor ) in southern California. We studied the response of pumas to natural and anthropogenic features within their home ranges and how mortality risk varied across a gradient of human development. We also used our housing-density model to estimate past and future housing densities and model the distribution of puma habitat in 1970, 2000, and 2030. The natural landscape for pumas in our study area consisted of riparian areas, oak woodlands, and open, conifer forests embedded in a chaparral matrix. Pumas rarely incorporated suburban or urban development into their home ranges, which is consistent with the hypothesis that the behavioral decisions of individuals can be collectively manifested as population-limiting factors at broader spatial scales. Pumas incorporated rural and exurban development into their home ranges, apparently perceiving these areas as modified, rather than non-habitat. Overall, pumas used exurban areas less than expected and showed a neutral response to rural areas. However, individual pumas that selected for or showed a neutral response to exurban areas had a higher risk of mortality than pumas that selected against exurban habitat. Exurban areas are likely hotspots for puma-human conflict in southern California. Approximately 10% of our study area will transform from exurban, rural, or undeveloped areas to suburban or urban by 2030, and 35% of suitable puma habitat on private land in 1970 will have been lost by 2030. These land-use changes will further isolate puma populations in southern California, but the ability to visualize these changes had provided a new tool for developing proactive conservation solutions.

California↗
USGS2011

Persistence and diversification of the Holarctic shrew, Sorex tundrensis (Family Soricidae), in response to climate change

Environmental processes govern demography, species movements, community turnover and diversification and yet in many respects these dynamics are still poorly understood at high latitudes. We investigate the combined effects of climate change and geography through time for a widespread Holarctic shrew, Sorex tundrensis . We include a comprehensive suite of closely related outgroup taxa and three independent loci to explore phylogeographic structure and historical demography. We then explore the implications of these findings for other members of boreal communities. The tundra shrew and its sister species, the Tien Shan shrew ( Sorex asper ), exhibit strong geographic population structure across Siberia and into Beringia illustrating local centres of endemism that correspond to Late Pleistocene refugia. Ecological niche predictions for both current and historical distributions indicate a model of persistence through time despite dramatic climate change. Species tree estimation under a coalescent process suggests that isolation between populations has been maintained across timeframes deeper than the periodicity of Pleistocene glacial cycling. That some species such as the tundra shrew have a history of persistence largely independent of changing climate, whereas other boreal species shifted their ranges in response to climate change, highlights the dynamic processes of community assembly at high latitudes.

Molecular Ecology↗
USGS2020

elfgen: A new instream flow framework for rapid generation and optimization of flow-ecology relations

Effective water resource management requires practical, data‐driven determination of instream flow needs. Newly developed, high‐resolution flow models and aquatic species databases provide enormous opportunity, but the volume of data can prove challenging to manage without automated tools. The objective of this study was to develop a framework of analytical methods and best practices to reduce costs of entry into flow–ecology analysis by integrating widely available hydrologic and ecological datasets. Ecological limit functions (ELFs) describing the relation between maximum species richness and stream size characteristics (streamflow or drainage area) were developed. Species richness is expected to increase with streamflow through a watershed up to a point where it either plateaus or transitions to a decreasing trend in larger streams. Our results show that identifying the location of this "breakpoint" is critical for producing optimal ELF model fit. We found that richness breakpoints can be estimated using automated low‐supervision methods, with high‐supervision providing negligible improvement in detection accuracy. Model fit (and predictive capability) was found to be superior in smaller hydrologic units. The ELF model ("elfgen" R package available on GitHub: https://github.com/HARPgroup/elfgen ) can be used to generate ELFs using built‐in datasets for the conterminous United States, or applied anywhere else streamflow and biodiversity data inputs are available.

Virginia↗
USGS2021

Demographic and potential biological removal models identify raptor species sensitive to current and future wind energy

A central challenge in applied ecology is understanding the effect of anthropogenic fatalities on wildlife populations and predicting which populations may be particularly vulnerable and in greatest need of management attention. We used 3 approaches to investigate potential effects of fatalities from collisions with wind turbines on 14 raptor species for both current (106 GW) and anticipated future (241 GW) levels of installed wind energy capacity in the United States. Our goals were to identify species at relatively high vs low risk of experiencing population declines from turbine collisions and to also compare results generated from these approaches. Two of the approaches used a calculated turbine-caused mortality rate to decrement population growth, where population trends were derived either from the North American Breeding Bird Survey or a matrix model parameterized from literature-derived demographic values. The third approach was potential biological removal, which estimates the number of fatalities that allow a population to reach and maintain its optimal sustainable population set by management concerns. Different results among the methods reveal substantial gaps in knowledge and uncertainty in both demographic parameters and species-specific estimates of fatalities from wind turbines. Our results suggest that, of the 14 species studied, those with relatively higher potential of population-level impacts from wind turbine collisions included barn owl, ferruginous hawk, golden eagle, American kestrel, and red-tailed hawk. Burrowing owl, Cooper’s hawk, great horned owl, northern harrier, turkey vulture, and osprey had a relatively lower potential for population impacts, and results were not easily interpretable for merlin, prairie falcon, and Swainson’s hawk. Projections of current levels of fatalities to future wind energy scenarios at 241 GW of installed capacity suggest some species could experience population declines because of turbine collisions. Populations of those species may benefit from research to identify tools to prevent or reduce raptor collisions with wind turbines.

Ecosphere↗
USGS2017

Integrating count and detection–nondetection data to model population dynamics

There is increasing need for methods that integrate multiple data types into a single analytical framework as the spatial and temporal scale of ecological research expands. Current work on this topic primarily focuses on combining capture–recapture data from marked individuals with other data types into integrated population models. Yet, studies of species distributions and trends often rely on data from unmarked individuals across broad scales where local abundance and environmental variables may vary. We present a modeling framework for integrating detection–nondetection and count data into a single analysis to estimate population dynamics, abundance, and individual detection probabilities during sampling. Our dynamic population model assumes that site-specific abundance can change over time according to survival of individuals and gains through reproduction and immigration. The observation process for each data type is modeled by assuming that every individual present at a site has an equal probability of being detected during sampling processes. We examine our modeling approach through a series of simulations illustrating the relative value of count vs. detection–nondetection data under a variety of parameter values and survey configurations. We also provide an empirical example of the model by combining long-term detection–nondetection data (1995–2014) with newly collected count data (2015–2016) from a growing population of Barred Owl ( Strix varia ) in the Pacific Northwest to examine the factors influencing population abundance over time. Our model provides a foundation for incorporating unmarked data within a single framework, even in cases where sampling processes yield different detection probabilities. This approach will be useful for survey design and to researchers interested in incorporating historical or citizen science data into analyses focused on understanding how demographic rates drive population abundance.

Ecology↗
USGS2007

Predicting the potential distribution of invasive exotic species using GIS and information-theoretic approaches: A case of ragweed (Ambrosia artemisiifolia L.) distribution in China

Invasive exotic species pose a growing threat to the economy, public health, and ecological integrity of nations worldwide. Explaining and predicting the spatial distribution of invasive exotic species is of great importance to prevention and early warning efforts. We are investigating the potential distribution of invasive exotic species, the environmental factors that influence these distributions, and the ability to predict them using statistical and information-theoretic approaches. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, for most species, absence data are not available. Presented with the challenge of developing a model based on presence-only information, we developed an improved logistic regression approach using Information Theory and Frequency Statistics to produce a relative suitability map. This paper generated a variety of distributions of ragweed (Ambrosia artemisiifolia L.) from logistic regression models applied to herbarium specimen location data and a suite of GIS layers including climatic, topographic, and land cover information. Our logistic regression model was based on Akaike's Information Criterion (AIC) from a suite of ecologically reasonable predictor variables. Based on the results we provided a new Frequency Statistical method to compartmentalize habitat-suitability in the native range. Finally, we used the model and the compartmentalized criterion developed in native ranges to "project" a potential distribution onto the exotic ranges to build habitat-suitability maps. ?? Science in China Press 2007.

Chinese Science Bulletin↗
USGS2010

Ecological contingency in the effects of climatic warming on forest herb communities

Downscaling from the predictions of general climate models is critical to current strategies for mitigating species loss caused by climate change. A key impediment to this downscaling is that we lack a fully developed understanding of how variation in physical, biological, or land-use characteristics mediates the effects of climate change on ecological communities within regions. We analyzed change in understory herb communities over a 60-y period (1949/1951–2007/2009) in a complex montane landscape (the Siskiyou Mountains, Oregon) where mean temperatures have increased 2 °C since 1948, similar to projections for other terrestrial communities. Our 185 sites included primary and secondary-growth lower montane forests (500–1.200 m above sea level) and primary upper montane to subalpine forests (1,500–2,100 m above sea level). In lower montane forests, regardless of land-use history, we found multiple herb-community changes consistent with an effectively drier climate, including lower mean specific leaf area, lower relative cover by species of northern biogeographic affinity, and greater compositional resemblance to communities in southerly topographic positions. At higher elevations we found qualitatively different and more modest changes, including increases in herbs of northern biogeographic affinity and in forest canopy cover. Our results provide community-level validation of predicted nonlinearities in climate change effects.

Oregon↗
USGS2017

Response of fish population dynamics to mitigation activities in a large regulated river

Extensive water development in large rivers has precipitated many negative ecological effects on native fish populations. Mitigation for such development often focuses on restoring biological integrity through remediation of the physical and chemical properties of regulated rivers. However, evaluating and defining the success of those programs can be difficult. We modeled the influence of mitigation-related environmental factors on growth and recruitment of two ecologically important native fish species (Largescale Sucker Catostomus macrocheilus and Mountain Whitefish Prosopium williamsoni ) in the Kootenai River, Idaho. Artificial nutrient (phosphorus) addition best predicted the variability in annual growth of both species. Nutrient addition was positively related to Largescale Sucker growth but negatively related to Mountain Whitefish growth. The best model explained 82% of the annual variability in incremental growth for Largescale Suckers and 61% of the annual variability for Mountain Whitefish. Year-class strength of Largescale Suckers was not closely related to any of the environmental variables evaluated; however, year-class strength of Mountain Whitefish was closely associated with nutrient addition, discharge, and temperature. Most research has focused on biotic assemblages to evaluate the effects of mitigation activities on fishes, but there is an increased need to identify the influence of rehabilitation activities on fish population dynamics within those assemblages. Here, we demonstrate how fish growth can serve as an indicator of rehabilitation success in a highly regulated large river. Future fish restoration projects can likely benefit from a change in scope and from consideration of an evaluation framework involving the response of population rate functions to mitigation.

Idaho↗